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suppressMessages(library(tidyverse))
suppressMessages(library(stringr))
suppressMessages(library(ISLR))
suppressMessages(library(caret))
suppressMessages(library(doMC))
registerDoMC(cores=4)
Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
Getting and proccesing the data
library(stringr)
myData = read.csv('/home/harpo/Dropbox/ongoing-work/git-repos/labeling-datasets/phd/datasets/data_all_result.txt', stringsAsFactors = F, sep = ' ')
#Create data backup
myData.bkup <- myData
#Create new column: length of model, and number of periodicity, duration and size characteristic in the model.
myData = myData %>% mutate(letter_count = nchar(State))
#Periodicity
myData = myData %>% mutate(strong_p = str_count(State,'[a-i]'))
myData = myData %>% mutate(weak_p = str_count(State,'[A-I]'))
myData = myData %>% mutate(weak_np = str_count(State,'[r-z]'))
myData = myData %>% mutate(strong_np = str_count(State,'[R-Z]'))
#Duration
myData = myData %>% mutate(duration_s = str_count(State,'(a|A|r|R|1|d|D|u|U|4|g|G|x|X|7)'))#c('a','A','r','R','1','d','D','u','U','4','g','G','x','X','7')))
myData = myData %>% mutate(duration_m = str_count(State,'(b|B|s|S|2|e|E|v|V|5|h|H|y|Y|8)'))#c('b','B','s','S','2','e','E','v','V','5','h','H','y','Y','8')))
myData = myData %>% mutate(duration_l = str_count(State,'(c|C|t|T|3|f|F|w|W|6|i|I|z|Z|9)'))#c('c','C','t','T','3','f','F','w','W','6','i','I','z','Z','9')))
#Size
myData = myData %>% mutate(size_s = str_count(State,'[a-c]') + str_count(State,'[A-C]') + str_count(State,'[r-t]') + str_count(State,'[R-T]') + str_count(State,'[1-3]'))
myData = myData %>% mutate(size_m = str_count(State,'[d-f]') + str_count(State,'[D-F]') + str_count(State,'[u-w]') + str_count(State,'[U-W]') + str_count(State,'[4-6]'))
myData = myData %>% mutate(size_l = str_count(State,'[g-i]') + str_count(State,'[G-I]') + str_count(State,'[x-z]') + str_count(State,'[X-Z]') + str_count(State,'[7-9]'))
#Remove from LabelName unnecessary characters (ej: V42, -17)
myData <- myData %>% mutate(LabelName = gsub('V[0-9]+-','',LabelName))
myData <- myData %>% mutate(LabelName = gsub('-[0-9]+','',LabelName))
myData <- myData %>% mutate(LabelName = gsub('CC[0-9]+-','CC-',LabelName))
#Keep only connection with more than 3 symbols
myData <- myData %>% filter(letter_count > 3)
#Periodicity %
myData <- myData %>% mutate(strong_p = (strong_p / letter_count))
myData <- myData %>% mutate(weak_p = (weak_p / letter_count))
myData <- myData %>% mutate(strong_np = (strong_np / letter_count))
myData <- myData %>% mutate(weak_np = (weak_np / letter_count))
#Duration %
myData <- myData %>% mutate(duration_s = (duration_s / letter_count))
myData <- myData %>% mutate(duration_m = (duration_m / letter_count))
myData <- myData %>% mutate(duration_l = (duration_l / letter_count))
#Size %
myData <- myData %>% mutate(size_s = (size_s / letter_count))
myData <- myData %>% mutate(size_m = (size_m / letter_count))
myData <- myData %>% mutate(size_l = (size_l / letter_count))
#head(myData)
myData[1:20,]
#Making feature vectors
feature_vectors = myData[,c('strong_p','weak_p','weak_np','strong_np','duration_s','duration_m','duration_l','size_s','size_m','size_l','letter_count','Label','LabelName')]
names(feature_vectors) = c("sp","wp","wnp","snp","ds","dm","dl","ss","sm","sl","length","class","subclass")
feature_vectors$class = factor(feature_vectors$class)
feature_vectors$subclass = factor(feature_vectors$subclass)
Create training set and testset
Random Forest Classificator
# Random Forest
rfFit <- train(class ~ .,
data = training,
metric="ROC",
method = "rf",
trControl = ctrl_fast)
rfFit
rfFit$finalModel
predsrfprobs=predict(rfFit,testing,type='prob')
predsrf=ifelse(predsrfprobs$Botnet >=0.9,'Botnet','Normal')
confusionMatrix(predsrf,testing$class)
library(ggplot2)
library(plotROC)
selectedIndices <- rfFit$pred$mtry == 2
ggplot(cbind(predsrfprobs,class=testing$class),
aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) +
geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') +
theme_bw()
cbind(predsrfprobs,class=testing$class)
KNN
#Checking distibution in origanl data and partitioned data
prop.table(table(training$class)) * 100
prop.table(table(testing$class)) * 100
prop.table(table(feature_vectors$class)) * 100
trainX <- training[,names(training) != "class"]
preProcValues <- preProcess(x = trainX,method = c("center", "scale"))
preProcValues
knnFit <- train(class ~ ., data = training, method = "knn", trControl = ctrl_fast, preProcess = c("center","scale"), tuneLength = 20)
#Output of kNN fit
knnFit
#Plotting yields Number of Neighbours Vs accuracy (based on repeated cross validation)
plot(knnFit)
knnPredict <- predict(knnFit,newdata = testing )
#Get the confusion matrix to see accuracy value and other parameter values
confusionMatrix(knnPredict, testing$class )
mean(knnPredict == testing$class)
library(pROC)
knnPredict <- predict(knnFit,newdata = testing , type="prob")
knnROC <- roc(testing$class,knnPredict[,"Botnet"], levels = c('Normal','Botnet'))#rev(testing$class))
knnROC
ggplot(cbind(knnPredict,class=testing$class),
aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) +
geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') +
theme_bw()
#plot(knnROC, type="S", print.thres= 0.5)
Logistic Regression
logicRFit <- train(class ~ ., method='glm', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training, family=binomial(link='logit'))
#logicRFit <- train(class ~ sp*wp*wnp*snp*ds*dm*dl*ss*sm*sl, method='glm', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training, family=binomial(link='logit'))
#logicRFit <- train(class ~ sp+wp+wnp+snp+ds+dm+dl+ss+sm+sl, method='glm', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training, family=binomial(link='logit'))
#summary(logicRFit)
#Output of Logistic Regression fit
logicRFit
logicRPredict <- predict(logicRFit, newdata = testing )
confusionMatrix(logicRPredict, testing$class)
logicRPredict <- predict(logicRFit, newdata = testing, type="prob")
logicROC <- roc(testing$class,logicRPredict[,"Botnet"], levels = c('Normal','Botnet'))#rev(testing$class))
ggplot(cbind(logicRPredict,class=testing$class),
aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) +
geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') +
theme_bw()
#logicROC
Naive Bayes
naiveBayesFit <- train(class ~ ., method='nb', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training)
naiveBayesFit
naiveBayesPredict <- predict(naiveBayesFit, newdata = testing)
confusionMatrix(naiveBayesPredict, testing$class)
naiveBayesPredict <- predict(naiveBayesFit, newdata = testing, type = 'prob')
naiveBayesROC <- roc(testing$class,naiveBayesPredict[,"Botnet"], levels = c('Normal','Botnet'))#rev(testing$class))
naiveBayesROC
ggplot(cbind(naiveBayesPredict,class=testing$class),
aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) +
geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') +
theme_bw()
#plot(naiveBayesROC, type="S", print.thres= 0.5)
Suport Vector Machine
svmFit <- train(class ~ ., method='svmLinear', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training, family=binomial(link='logit'))
svmFit
svmPredict <- predict(svmFit, newdata = testing)
confusionMatrix(svmPredict, testing$class)
svmPredict <- predict(svmFit, newdata = testing, type = "prob")
svmROC <- roc(testing$class,svmPredict[,"Botnet"], levels = c('Normal','Botnet'))#rev(testing$class))
svmROC
ggplot(cbind(svmPredict,class=testing$class),
aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) +
geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') +
theme_bw()
Comparing Models
resamps <- resamples(list(rf = rfFit, lr = logicRFit, nv = naiveBayesFit, svm = svmFit))
summary(resamps)
bwplot(resamps)
diffs <- diff(resamps)
summary(diffs)
values=resamps$values
values
names(values)[2]<-"rfSens"
ggplot(values)+
geom_boxplot(aes(y=rfSens,x=1))
Making probabilistic table.
# Botnet probabilistic table
botnet_prob_result = data.frame(testing$class, predsrfprobs$Botnet, knnPredict$Botnet, logicRPredict$Botnet, naiveBayesPredict$Botnet ,svmPredict$Botnet)
names(botnet_prob_result) = c('TrueClass','RamdomForest','KNN','LogisticRegression', 'NaiveBayes', 'SVM','subclass')
#load("./botnet_prob_results.Rda")
library(grid)
library(gridExtra)
botnet_prob_result %>% group_by(subclass) %>% summarise(n=n(),mean=mean(KNN),sd=sd(KNN)) %>% arrange(desc(n))
botnet_prob_result %>% group_by(subclass) %>% summarise(n=n(),mean=mean(NaiveBayes),sd=sd(NaiveBayes)) %>% arrange(desc(n))%>% top_n(10)
Selecting by sd
botnet_10_top<-botnet_prob_result %>% group_by(subclass) %>% summarise(n=n()) %>% arrange(desc(n))%>% top_n(10)
Selecting by n
botnet_10_top<-inner_join(botnet_10_top,botnet_prob_result,by="subclass")
knn_plot<-bwplot(subclass~KNN,data=botnet_10_top,do.out = FALSE,scales=list(y=list(draw=FALSE)))
rf_plot<-bwplot(subclass~RamdomForest,data=botnet_10_top,do.out = FALSE,scales=list(y=list(draw=FALSE)))
rl_plot<-bwplot(subclass~LogisticRegression,data=botnet_10_top,do.out = FALSE,scales=list(y=list(draw=FALSE)))
svm_plot<-bwplot(subclass~SVM,data=botnet_10_top,do.out = FALSE,scales=list(y=list(draw=FALSE)))
pl = list(knn_plot, rf_plot,rl_plot,svm_plot)
# do.call(grid.arrange, c(pl, nrow=1))
do.call(grid.arrange, c(lapply(pl, update), list(nrow=1)))

some heatmaps

Clustering and PCA
kmeans_mod<-kmeans(testing[,1:10],centers = 10)
testing_cluster<-cbind(testing,cluster=kmeans_mod$cluster)
pca<-prcomp(testing[,c(-11,-12)], center = TRUE, scale. = TRUE)
pca_testing<-data.frame(pca$x,class=testing_cluster$class,
subclass=testing_cluster$subclass,
cluster=testing_cluster$cluster
)
g<-ggplot(pca_testing,aes(x=PC1,y=PC2))+
geom_jitter(aes(color=as.factor(subclass),text=cluster,shape=class))+
#geom_point(aes(shape=asignacion),size=3)+
ylab("PC1")+xlab("PC2")+
theme_classic()+
#scale_shape_manual(values=c(8,6))+
guides(color=FALSE,alpha=FALSE)
Ignoring unknown aesthetics: text
ggplotly(g)
#Normal probabilistic table
normal_prob_result = data.frame(testing$class, predsrfprobs$Normal, knnPredict$Normal, logicRPredict$Normal, naiveBayesPredict$Normal ,svmPredict$Normal)
names(normal_prob_result) = c('True Class','Ramdom Forest','KNN','Logistic Regression', 'Naive Bayes', 'Suport VM')
normal_prob_result
---
title: "Connection Classification"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
suppressMessages(library(tidyverse))
suppressMessages(library(stringr))
suppressMessages(library(ISLR))
suppressMessages(library(caret))
suppressMessages(library(doMC))
library(plotly)
registerDoMC(cores=4)

```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

### Getting and proccesing the data
```{r}
library(stringr)
myData = read.csv('/home/harpo/Dropbox/ongoing-work/git-repos/labeling-datasets/phd/datasets/data_all_result.txt', stringsAsFactors = F, sep = ' ')

#Create data backup
myData.bkup <- myData
#Create new column: length of model, and number of periodicity, duration and size characteristic in the model.
myData = myData %>% mutate(letter_count = nchar(State))
#Periodicity
myData = myData %>% mutate(strong_p = str_count(State,'[a-i]'))
myData = myData %>% mutate(weak_p = str_count(State,'[A-I]'))
myData = myData %>% mutate(weak_np = str_count(State,'[r-z]'))
myData = myData %>% mutate(strong_np = str_count(State,'[R-Z]'))
#Duration
myData = myData %>% mutate(duration_s = str_count(State,'(a|A|r|R|1|d|D|u|U|4|g|G|x|X|7)'))#c('a','A','r','R','1','d','D','u','U','4','g','G','x','X','7')))
myData = myData %>% mutate(duration_m = str_count(State,'(b|B|s|S|2|e|E|v|V|5|h|H|y|Y|8)'))#c('b','B','s','S','2','e','E','v','V','5','h','H','y','Y','8')))
myData = myData %>% mutate(duration_l = str_count(State,'(c|C|t|T|3|f|F|w|W|6|i|I|z|Z|9)'))#c('c','C','t','T','3','f','F','w','W','6','i','I','z','Z','9')))
#Size
myData = myData %>% mutate(size_s = str_count(State,'[a-c]') + str_count(State,'[A-C]') + str_count(State,'[r-t]') + str_count(State,'[R-T]') + str_count(State,'[1-3]'))
myData = myData %>% mutate(size_m = str_count(State,'[d-f]') + str_count(State,'[D-F]') + str_count(State,'[u-w]') + str_count(State,'[U-W]') + str_count(State,'[4-6]'))
myData = myData %>% mutate(size_l = str_count(State,'[g-i]') + str_count(State,'[G-I]') + str_count(State,'[x-z]') + str_count(State,'[X-Z]') + str_count(State,'[7-9]'))

#Remove from LabelName unnecessary characters (ej: V42, -17)
myData <- myData %>% mutate(LabelName = gsub('V[0-9]+-','',LabelName))
myData <- myData %>% mutate(LabelName = gsub('-[0-9]+','',LabelName))
myData <- myData %>% mutate(LabelName = gsub('CC[0-9]+-','CC-',LabelName))

#Keep only connection with more than 3 symbols
myData <- myData %>% filter(letter_count > 3)

#Periodicity %
myData <- myData %>% mutate(strong_p = (strong_p / letter_count))
myData <- myData %>% mutate(weak_p = (weak_p / letter_count))
myData <- myData %>% mutate(strong_np = (strong_np / letter_count))
myData <- myData %>% mutate(weak_np = (weak_np / letter_count))
#Duration %
myData <- myData %>% mutate(duration_s = (duration_s / letter_count))
myData <- myData %>% mutate(duration_m = (duration_m / letter_count))
myData <- myData %>% mutate(duration_l = (duration_l / letter_count))
#Size %
myData <- myData %>% mutate(size_s = (size_s / letter_count))
myData <- myData %>% mutate(size_m = (size_m / letter_count))
myData <- myData %>% mutate(size_l = (size_l / letter_count))

#head(myData)
myData[1:20,]

#Making feature vectors
feature_vectors = myData[,c('strong_p','weak_p','weak_np','strong_np','duration_s','duration_m','duration_l','size_s','size_m','size_l','letter_count','Label','LabelName')]
names(feature_vectors) = c("sp","wp","wnp","snp","ds","dm","dl","ss","sm","sl","length","class","subclass")
feature_vectors$class = factor(feature_vectors$class)
feature_vectors$subclass = factor(feature_vectors$subclass)
```

### Create training set and testset
```{r}
set.seed(300)
trainIndex <- createDataPartition(feature_vectors$class, p=0.80, list=FALSE)
data_train <- feature_vectors[ trainIndex,]
data_test <- feature_vectors[-trainIndex,]

#data_train = data_train %>% filter(length>5)
train <- upSample(x = data_train,  y = data_train$class, yname="class")
#train <- upSample(x = train,  y = train$subclass, yname="class")
training <- train[,-c(11,12)]
testing <- data_test[,-c(11)]
training

ctrl_fast <- trainControl(method="cv", 
                     repeats=2,
                     number=10, 
                     summaryFunction=twoClassSummary,
                     verboseIter=T,
                     classProbs=TRUE,
                     allowParallel = TRUE)  
ctrl <- trainControl(method="repeatedcv",repeats = 3) #,classProbs=TRUE,summaryFunction = twoClassSummary)
```


### Random Forest Classificator
```{r}
  # Random Forest
rfFit <- train(class ~ .,
               data = training,
               metric="ROC",
               method = "rf",
               trControl = ctrl_fast)

rfFit
rfFit$finalModel
```

```{r}
predsrfprobs=predict(rfFit,testing,type='prob')
predsrf=ifelse(predsrfprobs$Botnet >=0.9,'Botnet','Normal')
confusionMatrix(predsrf,testing$class)
```

```{r}
library(ggplot2)
library(plotROC)
selectedIndices <- rfFit$pred$mtry == 2
ggplot(cbind(predsrfprobs,class=testing$class), 
       aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) + 
    geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') + 
  theme_bw()

cbind(predsrfprobs,class=testing$class)
```


### KNN
```{r}
#Checking distibution in origanl data and partitioned data
prop.table(table(training$class)) * 100
prop.table(table(testing$class)) * 100
prop.table(table(feature_vectors$class)) * 100

trainX <- training[,names(training) != "class"]
preProcValues <- preProcess(x = trainX,method = c("center", "scale"))
preProcValues
```
```{r}
knnFit <- train(class ~ ., data = training, method = "knn", trControl = ctrl_fast, preProcess = c("center","scale"), tuneLength = 20)

#Output of kNN fit
knnFit
```
```{r}
#Plotting yields Number of Neighbours Vs accuracy (based on repeated cross validation)
plot(knnFit)
```
```{r}
knnPredict <- predict(knnFit,newdata = testing )
#Get the confusion matrix to see accuracy value and other parameter values
confusionMatrix(knnPredict, testing$class )
```
```{r}
mean(knnPredict == testing$class)
```
```{r}
library(pROC)
knnPredict <- predict(knnFit,newdata = testing , type="prob")
knnROC <- roc(testing$class,knnPredict[,"Botnet"], levels = c('Normal','Botnet'))#rev(testing$class))
knnROC
```

```{r}
ggplot(cbind(knnPredict,class=testing$class), 
       aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) + 
    geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') + 
  theme_bw()

#plot(knnROC, type="S", print.thres= 0.5)
```
### Logistic Regression

```{r}
logicRFit <- train(class ~ ., method='glm', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training, family=binomial(link='logit'))
#logicRFit <- train(class ~ sp*wp*wnp*snp*ds*dm*dl*ss*sm*sl, method='glm', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training, family=binomial(link='logit'))
#logicRFit <- train(class ~ sp+wp+wnp+snp+ds+dm+dl+ss+sm+sl, method='glm', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training, family=binomial(link='logit'))

#summary(logicRFit)
#Output of Logistic Regression fit
logicRFit
```

```{r}

logicRPredict <- predict(logicRFit, newdata = testing )

confusionMatrix(logicRPredict, testing$class)
```
```{r}
logicRPredict <- predict(logicRFit, newdata = testing, type="prob")
logicROC <- roc(testing$class,logicRPredict[,"Botnet"], levels = c('Normal','Botnet'))#rev(testing$class))

ggplot(cbind(logicRPredict,class=testing$class), 
       aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) + 
    geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') + 
  theme_bw()

#logicROC
```

### Naive Bayes
```{r}
naiveBayesFit <- train(class ~ ., method='nb', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training)
naiveBayesFit
```

```{r}
naiveBayesPredict <- predict(naiveBayesFit, newdata = testing)

confusionMatrix(naiveBayesPredict, testing$class)
```
```{r}
naiveBayesPredict <- predict(naiveBayesFit, newdata = testing, type = 'prob')
naiveBayesROC <- roc(testing$class,naiveBayesPredict[,"Botnet"], levels = c('Normal','Botnet'))#rev(testing$class))
naiveBayesROC

ggplot(cbind(naiveBayesPredict,class=testing$class), 
       aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) + 
    geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') + 
  theme_bw()

#plot(naiveBayesROC, type="S", print.thres= 0.5)
```


### Suport Vector Machine
```{r}
svmFit <- train(class ~ ., method='svmLinear', trControl = ctrl_fast,preProcess=c('scale', 'center'), data=training, family=binomial(link='logit'))
svmFit
```

```{r}
svmPredict <- predict(svmFit, newdata = testing)
confusionMatrix(svmPredict, testing$class)
```

```{r}
svmPredict <- predict(svmFit, newdata = testing, type = "prob")

svmROC <- roc(testing$class,svmPredict[,"Botnet"], levels = c('Normal','Botnet'))#rev(testing$class))
svmROC

ggplot(cbind(svmPredict,class=testing$class), 
       aes(m = Botnet, d = factor(class, labels=c("Normal","Botnet"),levels = c("Normal", "Botnet")))) + 
    geom_roc(hjust = -0.4, vjust = 1.5,colour='orange') + 
  theme_bw()

```


### Comparing Models
```{r}
resamps <- resamples(list(rf = rfFit, lr = logicRFit, nv = naiveBayesFit, svm = svmFit))
summary(resamps)
bwplot(resamps)
diffs <- diff(resamps)
summary(diffs)
values=resamps$values
values
names(values)[2]<-"rfSens"

ggplot(values)+
  geom_boxplot(aes(y=rfSens,x=1))

```
### Making probabilistic table.
```{r}
# Botnet probabilistic table
botnet_prob_result = data.frame(testing$class, predsrfprobs$Botnet, knnPredict$Botnet, logicRPredict$Botnet, naiveBayesPredict$Botnet ,svmPredict$Botnet)
names(botnet_prob_result) = c('TrueClass','RamdomForest','KNN','LogisticRegression', 'NaiveBayes', 'SVM','subclass')
```


```{r}
#load("./botnet_prob_results.Rda")
library(grid)
library(gridExtra)
botnet_prob_result %>% group_by(subclass) %>% summarise(n=n(),mean=mean(KNN),sd=sd(KNN)) %>% arrange(desc(n))
botnet_prob_result %>% group_by(subclass) %>% summarise(n=n(),mean=mean(NaiveBayes),sd=sd(NaiveBayes)) %>% arrange(desc(n))%>% top_n(10)
botnet_10_top<-botnet_prob_result %>% group_by(subclass) %>% summarise(n=n()) %>% arrange(desc(n))%>% top_n(10)
botnet_10_top<-inner_join(botnet_10_top,botnet_prob_result,by="subclass")

knn_plot<-bwplot(subclass~KNN,data=botnet_10_top,do.out = FALSE,scales=list(y=list(draw=FALSE)))
rf_plot<-bwplot(subclass~RamdomForest,data=botnet_10_top,do.out = FALSE,scales=list(y=list(draw=FALSE)))
rl_plot<-bwplot(subclass~LogisticRegression,data=botnet_10_top,do.out = FALSE,scales=list(y=list(draw=FALSE)))
svm_plot<-bwplot(subclass~SVM,data=botnet_10_top,do.out = FALSE,scales=list(y=list(draw=FALSE)))


pl = list(knn_plot, rf_plot,rl_plot,svm_plot)
# do.call(grid.arrange, c(pl, nrow=1))
do.call(grid.arrange, c(lapply(pl, update), list(nrow=1)))

```

#### some heatmaps
```{r heatmaps}
library(scales)
knn_m<-matrix(botnet_prob_result[1:1830,]$KNN,ncol=30,nrow=61)
svm_m<-matrix(botnet_prob_result[1:1830,]$SVM,ncol=30,nrow=61)
lr_m<-matrix(botnet_prob_result[1:1830,]$LogisticRegression,ncol=30,nrow=61)
nb_m<-matrix(botnet_prob_result[1:1830,]$NaiveBayes,ncol=30,nrow=61)
rf_m<-matrix(botnet_prob_result[1:1830,]$RamdomForest,ncol=30,nrow=61)

mdf<-as.data.frame(knn_m)
mdf<-cbind(mdf,id=seq(1:61))
mdf<-reshape2::melt(mdf,id.vars=c("id"))
h1<-ggplot(mdf)+
  geom_tile(aes(x=id,y=variable,fill=value),
            colour = "white") +
  scale_fill_gradient(low = "white",
    high = "orange")+ylab("")+xlab("")+
  guides(fill=FALSE)


mdf<-as.data.frame(svm_m)
mdf<-cbind(mdf,id=seq(1:61))
mdf<-reshape2::melt(mdf,id.vars=c("id"))
h2<-ggplot(mdf)+
  geom_tile(aes(x=id,y=variable,fill=value),
            colour = "white") +
  scale_fill_gradient(low = "white",
    high = "orange")+ylab("")+xlab("")+
  guides(fill=FALSE)


mdf<-as.data.frame(rf_m)
mdf<-cbind(mdf,id=seq(1:61))
mdf<-reshape2::melt(mdf,id.vars=c("id"))
h3<-ggplot(mdf)+
  geom_tile(aes(x=id,y=variable,fill=value),
            colour = "white") +
  scale_fill_gradient(low = "white",
    high = "orange")+ylab("")+xlab("")+
  guides(fill=FALSE)


mdf<-as.data.frame(nb_m)
mdf<-cbind(mdf,id=seq(1:61))
mdf<-reshape2::melt(mdf,id.vars=c("id"))
h4<-ggplot(mdf)+
  geom_tile(aes(x=id,y=variable,fill=value),
            colour = "white") +
  scale_fill_gradient(low = "white",
    high = "orange")+ylab("")+xlab("")+
  guides(fill=FALSE)
grid.arrange(h1,h2,h3,h4,ncol=2,nrow=2)
```

#### difference heatmaps
```{r heatmap diff}
mdf<-as.data.frame(rf_m - knn_m)
mdf<-cbind(mdf,id=seq(1:61))
mdf<-reshape2::melt(mdf,id.vars=c("id"))
mdf<-cbind(mdf,subclass=(botnet_prob_result[1:1830,]$subclass))
diff<-ggplot(mdf)+
  geom_tile(aes(x=id,y=variable,fill=value,text=subclass),
            colour = "white") +
  scale_fill_gradientn(colours=c("red","white","green"),
           values  = rescale(c(min(mdf$value), 0.05, max(mdf$value))))+
           guides(fill=FALSE)+theme_bw()  
ggplotly(diff)
d3heatmap(rf_m - knn_m,colors = "Blues",cellnote=matrix(botnet_prob_result[1:1830,]$subclass,ncol=30,nrow=61))
```

### Clustering and PCA
```{r clustering}

kmeans_mod<-kmeans(testing[,1:10],centers = 10)
testing_cluster<-cbind(testing,cluster=kmeans_mod$cluster)
pca<-prcomp(testing[,c(-11,-12)], center = TRUE, scale. = TRUE) 
pca_testing<-data.frame(pca$x,class=testing_cluster$class,
                              subclass=testing_cluster$subclass,
                               cluster=testing_cluster$cluster
                               )
g<-ggplot(pca_testing,aes(x=PC1,y=PC2))+
  geom_jitter(aes(color=as.factor(subclass),text=cluster,shape=class))+
  #geom_point(aes(shape=asignacion),size=3)+
  ylab("PC1")+xlab("PC2")+
  theme_classic()+
#scale_shape_manual(values=c(8,6))+
   guides(color=FALSE,alpha=FALSE)
ggplotly(g)

```

```{r}
#Normal probabilistic table
normal_prob_result = data.frame(testing$class, predsrfprobs$Normal, knnPredict$Normal, logicRPredict$Normal, naiveBayesPredict$Normal ,svmPredict$Normal)
names(normal_prob_result) = c('True Class','Ramdom Forest','KNN','Logistic Regression', 'Naive Bayes', 'Suport VM')
normal_prob_result
```

